Proteins play a central role in living organisms, serving, for example, as enzymes that catalyze metabolic reactions, as structural or mechanical units, and as cell signaling molecules. The field of proteomics, which entails large scale identification and structural characterization of proteins, depends strongly on tandem mass spectrometry (MS/MS) and database searches that make use of MS/MS spectra. The overarching goal of this proposal is to improve automated peptide and protein identification by tandem mass spectrometry. The proposed research will identify and comprehensively characterize clusters of fragmentation behavior for peptides activated by two major complementary MS/MS activation methods, electron transfer dissociation (ETD) and collisionally- activated dissociation (CAD), when both are applied in two prominent instrument platforms used for proteomics experiments (linear ion trap and quadrupole time-of-flight, QTOF). The underlying hypothesis guiding this research is that the computer algorithms that are used for peptide and protein sequencing, identification, and quantitation, and the embedded data acquisition software, can be improved by statistically analyzing how peptides fragment at a molecular level. A priori knowledge of the behavior of differenct amino acids provides significant insight into the possible fragmentation patterns observed and the MS/MS spectra and these data will be used to improve data acquisition and data analysis. This research brings together three research groups, the Wysocki group with expertise in peptide fragmentation mechanisms and data mining, the Tseng group with expertise in biostatistics/ bioinformatics( to perform clustering plus classification and regression tree analysis of large spectral datasets), and the Coon group, developers of electron transfer dissociation (ETD) in the linear ion trap/Orbitrap mass spectrometer.